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Several approaches of structuring (factorization, decomposition) ofDempster-Shafer joint belief functions from literature are reviewed withspecial emphasis on their capability to capture independence from the point ofview of the claim that belief functions generalize bayes notion of probability.  It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'{Smets:93} directed acyclic graphs are unable to capture statisticaldependence/independence of bayesian networks {Pearl:88}. On the other hand,though Shenoy and Shafer's hypergraphs can explicitly represent bayesiannetwork factorization of bayesian belief functions, they disclaim any need forrepresentation of independence of variables in belief functions.  Cano et al. {Cano:93} reject the hypergraph representation of Shenoy andShafer just on grounds of missing representation of variable independence, butin their frameworks some belief functions factorizable in Shenoy/Shaferframework cannot be factored.  The approach in {Klopotek:93f} on the other hand combines the merits of bothCano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach nosimpler factorization than that in {Klopotek:93f} approach exists and on theother hand all independences among variables captured in Cano et al. frameworkand many more are captured in {Klopotek:93f} approach.%

Independence, Conditionality and Structure of Dempster-Shafer Belief Functions